This paper proposes a chaos based Frequency Hopped Spread Spectrum communication system. It is always desirable to use, large sets of frequency hopping sequences with large period, large linear span and good Hamming correlation in multiple access applications. Most of the FH systems are based on either PN sequences or Reed-Solomon codes, which are potentially weak, especially when there is intelligent jamming. In this context, a new class of sequences, based on chaotic systems, refered to as chaotic sequences, are used for frequency hopping. Frequency Hopping systems based on the iterated map, the so-called ‘Difference Map’ is proposed in this paper. Unlike the m-sequences, the number and length of such sequences are not restricted. Also, the noise-like appearance of such sequences can result in low probability of intercept.

This paper discusses the designing, implementation and testing of an Autonomous Underwater Vehicle (AUV) robot, which travels underwater to detect possible human presence. This robot is expected to be extremely useful in human rescue operations during water accidents. The AUV is deployed above the water surface, near to the approximate target location and it swims underwater, checking for the target (human body), in real time. Once the target is detected, the AUV locks the targets position and move towards the target to verify and make a decision: whether it is a human body or not. After the confirmation, the robot swims back vertically up to the water surface, from where it can be easily noticed by the ground rescue crew. The diver can then dive there, to find the human body. Alternately, an air bag could also be inflated after locking it to the target, which will float back to the surface carrying the target. This technology is very helpful for underwater human detection since we need not have to search the whole water body and hence both time and resources are saved.

Multi-valued logic (MVL) is that logic which has two or more logic values. In complex digital circuits, MVL (mainly Ternary logic) offers several advantages over binary logic. Carbon Nanotube Field Effect Transistor (CNTFET) technology is ideal to implement ternary logic circuits because of the threshold voltage of CNTFETs depends on the physical dimensions (chirality) of their channel. This work presents the implementation of a three-bit Ternary Prefix Adder using CNTFET technology. In this paper, a carry propagate-generate concept is used in order to implement the ternary prefix adder. A Kogge-Stone based prefix network is preferred for carry computation due to its high performance. A low power enoder is used for reducing the overall power of the adder. HSpice tool is chosen for designing this system. Simulation results show that there is a significant reduction in Power Delay Product (PDP) by 28 % compared to all other previous works.

Even though, innumerable approaches have been proposed for holistic face recognition, problems caused by occlusions received less attention in the literature. However, partial faces frequently appear in many real time situations. Facial occlusions (by sunglasses, hat/cap, scarf, and beard) can significantly deteriorate the performances of face recognition systems under unconstrained scenarios. In such situations, algorithms developed under holistic face, results in catastrophic performance. In this paper, we have proposed a scale and rotation invariant wavelet feature transform for partial face recognition. Partial faces at different orientations are considered here for experimentation. Biorthogonal wavelet basis (4.4) is employed for obtaining the Discrete Wavelet Transform of the images. The scale invariant feature transform (SIFT) is then applied on low-low (LL) and high-high (HH) subbands of the images. Results obtained with wavelet SIFT method is compared with SIFT and appearance based face recognition technique (PCA) over (Milborrow / University of Cape Town) MUCT database. Experimental studies with 100 subjects show that the proposed method improves recognition accuracy and reduces false acceptance rate (FAR) and false rejection rate (FRR).

Recent years have observed an immense refinement in the domain of object localization, especially over remote sensing images. Object localization aims to predict the objects within an image as well as its boundaries. Achieving accurate object localization over remote sensing images is always challenging due to the complex context information. The paper mainly concentrates on solving the problem of accurate object localization using a three-stage localization pipeline. The introductory stage in the pipeline focus on generating regions of interest (RoI) called candidate regions using selective search algorithm. Then, each of these candidate features is extracted by passing it through a hybrid convolutional neural network (CNN) model. Finally, to improve the localization accuracy we propose an optimal object localization technique called Unsupervised Score Based Bounding Box Regression using Feature Descriptors (USBBBR-FD) algorithm integrated with non-maximum suppression (NMS) algorithm to optimize the bounding boxes of regions which are detected as objects. Analysis shows that the detection precision of the hybrid CNN model is higher than that of any other single CNN model and the dimension-reduction CNN model performs far better than retrained CNN models. Experiments show that the proposed USBBBR-FD algorithm can more accurately locate objects within remote sensing images and also shows robustness in complex background as compared with traditional features extraction methods.

Brushless motors have come to monopolize over many applications such as automobile industries, medical instruments, aerospace applications etc. The features such as high power density, high reliability, compact size, wide range of speed control etc make brushless dc (BLDC) motor distinct from other motors. The ordinary control methods of BLDC conduce high ripples in torque and speed. Here an unaccustomed method is used for controlling the BLDC motor. A firefly algorithm technique with proportional-integral-derivative (PID) controller is used to elevate the dynamic control of BLDC motor. Compared to any other optimization techniques, firefly algorithm (FA) is most effective tool and it gives surpassing performance in the field of optimization. Here PID controller based FA technique is used, which results in torque and speed ripple reduction, and gives a better output.

The age of conventional vehicles based on fossil fuel is nearing an end, clearly due to their associated environmental issues and the best solution at present for substituting these conventional vehicles is none other than electric vehicles. Hence, electric vehicles are the future in terms of transportation and one of the most critical constraint for its deployment in a large scale is indeed its charging process with the available number of charging stations. This constraint can be solved to a certain extent if the user can be provided with information regarding charging stations nearby. Also, the availability of internet has improved a lot and is still developing, thus within a few years it is almost certain that reliable internet connectivity will be available almost everywhere making the deployment of Internet of Things and its related applications simpler and more practical. Our intention here is to provide a solution to the above constraint by introducing a system based on the concept of Internet of Things, which consist of an add on device for electric vehicles, a server, priority estimator for charging stations, all of which are interconnected through the internet and hence forming the internet of electric vehicles. The add on device takes inputs from OBD (On Board Diagnostics) port which is interfaced with CAN bus of the electric vehicle and a GPS module, the inputs acquired are pushed to a server using Wi-Fi internet connectivity available in the vehicles. The server then forwards the data received to all charging stations within a specified distance. The charging stations estimate the priority list and sends the list back to the server. The server on receiving the priority list from a charging station processes the list and sends information like priority of the electric vehicle, charging station ID and its location to each electric vehicle. The add-on device then displays the received information to the user when required so that the user could decide the optimum charging station to charge his/her electric vehicle. Thus, the system enables efficient and optimum utilization of charging stations by assisting the user to decide where to charge his/her electric vehicle.

Induction motor with indirect field oriented control is very suitable for high performance applications due to its excellent dynamic characteristics. Many artificial intelligence techniques and random search methods have been employed to improve the speed controller performance. This paper proposes a simplified indirect vector control strategy that can be realized in the absence of current and voltage sensors using fuzzy-logic speed controller. The fuzzy logic controller (FLC) uses seven membership functions for each parameter for the effective control of the drive system. The performance of the proposed system has been analyzed through simulation using MATLAB/Simulink package for different operating conditions such as sudden change in command speed and load conditions.

EEG represents electrical activity of the brain. Various brain disorders can be analyzed from the information that can be extracted from EEG signal. Extraction of relevant information from EEG is an arduous task due to the highly chaotic nature of EEG signal. Various signal processing techniques can be employed for this process. Variational mode decomposition(VMD) technique involves decomposition of EEG into sub bands characterized by limited bandwidth and characterized by center frequency. The value of center frequency is extracted for the sub bands and the value is analyzed as a parameter for diagnosing normal EEG and diseased EEG.

The term Content-based image retrieval (CBIR) is widely used to describe the process of retrieving desired images from a large database on the basis of features that can be automatically extracted from the images themselves. Earlier CBIR methods were based on extracting the low-level features of the image like shape, color, texture etc. But such systems lacked efficiency because the image concepts were not correctly identified. So there is need for an intelligent system to identify the concepts correctly. Semantic segmentation methods help in this regard by analyzing an image region wise. The introduction of deep learning techniques have brought about a significant performance improvement in semantic image segmentation methods. In this paper DeepLab v3 is used for semantic segmentation and ResNet-34 is used for further classification and image retrieval. Segmentation is performed on PASCAL VOC 2012 dataset, the regions extracted from this is further used for classification. This method provides superior results to other baseline methods.